Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations101223
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.3 MiB
Average record size in memory96.0 B

Variable types

Numeric9
Categorical2

Alerts

num_procedures has 46212 (45.7%) zeros Zeros

Reproduction

Analysis started2025-05-01 05:26:03.141637
Analysis finished2025-05-01 05:26:14.212207
Duration11.07 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.959663
Minimum5
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-01T05:26:14.282460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q155
median65
Q375
95-th percentile85
Maximum95
Range90
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.95113
Coefficient of variation (CV)0.24183158
Kurtosis0.27612077
Mean65.959663
Median Absolute Deviation (MAD)10
Skewness-0.62882939
Sum6676635
Variance254.43854
MonotonicityNot monotonic
2025-05-01T05:26:14.398188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
75 25888
25.6%
65 22365
22.1%
55 17150
16.9%
85 17107
16.9%
45 9654
 
9.5%
35 3768
 
3.7%
95 2792
 
2.8%
25 1653
 
1.6%
15 686
 
0.7%
5 160
 
0.2%
ValueCountFrequency (%)
5 160
 
0.2%
15 686
 
0.7%
25 1653
 
1.6%
35 3768
 
3.7%
45 9654
 
9.5%
55 17150
16.9%
65 22365
22.1%
75 25888
25.6%
85 17107
16.9%
95 2792
 
2.8%
ValueCountFrequency (%)
95 2792
 
2.8%
85 17107
16.9%
75 25888
25.6%
65 22365
22.1%
55 17150
16.9%
45 9654
 
9.5%
35 3768
 
3.7%
25 1653
 
1.6%
15 686
 
0.7%
5 160
 
0.2%

admission_type_id
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0282248
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-01T05:26:14.504807image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4471052
Coefficient of variation (CV)0.71348365
Kurtosis1.9234629
Mean2.0282248
Median Absolute Deviation (MAD)0
Skewness1.5866938
Sum205303
Variance2.0941135
MonotonicityNot monotonic
2025-05-01T05:26:14.623121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 53509
52.9%
3 18831
 
18.6%
2 18465
 
18.2%
6 5289
 
5.2%
5 4778
 
4.7%
8 320
 
0.3%
7 21
 
< 0.1%
4 10
 
< 0.1%
ValueCountFrequency (%)
1 53509
52.9%
2 18465
 
18.2%
3 18831
 
18.6%
4 10
 
< 0.1%
5 4778
 
4.7%
6 5289
 
5.2%
7 21
 
< 0.1%
8 320
 
0.3%
ValueCountFrequency (%)
8 320
 
0.3%
7 21
 
< 0.1%
6 5289
 
5.2%
5 4778
 
4.7%
4 10
 
< 0.1%
3 18831
 
18.6%
2 18465
 
18.2%
1 53509
52.9%

discharge_disposition_id
Real number (ℝ)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7275323
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-01T05:26:14.751526image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.2900007
Coefficient of variation (CV)1.4191697
Kurtosis5.9566599
Mean3.7275323
Median Absolute Deviation (MAD)0
Skewness2.5550926
Sum377312
Variance27.984108
MonotonicityNot monotonic
2025-05-01T05:26:14.876125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 59764
59.0%
3 13907
 
13.7%
6 12879
 
12.7%
18 3690
 
3.6%
2 2128
 
2.1%
22 1992
 
2.0%
11 1642
 
1.6%
5 1184
 
1.2%
25 988
 
1.0%
4 815
 
0.8%
Other values (16) 2234
 
2.2%
ValueCountFrequency (%)
1 59764
59.0%
2 2128
 
2.1%
3 13907
 
13.7%
4 815
 
0.8%
5 1184
 
1.2%
6 12879
 
12.7%
7 623
 
0.6%
8 108
 
0.1%
9 21
 
< 0.1%
10 6
 
< 0.1%
ValueCountFrequency (%)
28 139
 
0.1%
27 5
 
< 0.1%
25 988
 
1.0%
24 48
 
< 0.1%
23 412
 
0.4%
22 1992
2.0%
20 2
 
< 0.1%
19 8
 
< 0.1%
18 3690
3.6%
17 14
 
< 0.1%

admission_source_id
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7495036
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-01T05:26:14.993463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0712476
Coefficient of variation (CV)0.70810419
Kurtosis1.7352943
Mean5.7495036
Median Absolute Deviation (MAD)0
Skewness1.0321366
Sum581982
Variance16.575057
MonotonicityNot monotonic
2025-05-01T05:26:15.116854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7 57011
56.3%
1 29519
29.2%
17 6771
 
6.7%
4 3187
 
3.1%
6 2261
 
2.2%
2 1104
 
1.1%
5 855
 
0.8%
3 187
 
0.2%
20 161
 
0.2%
9 124
 
0.1%
Other values (7) 43
 
< 0.1%
ValueCountFrequency (%)
1 29519
29.2%
2 1104
 
1.1%
3 187
 
0.2%
4 3187
 
3.1%
5 855
 
0.8%
6 2261
 
2.2%
7 57011
56.3%
8 16
 
< 0.1%
9 124
 
0.1%
10 8
 
< 0.1%
ValueCountFrequency (%)
25 2
 
< 0.1%
22 12
 
< 0.1%
20 161
 
0.2%
17 6771
6.7%
14 2
 
< 0.1%
13 1
 
< 0.1%
11 2
 
< 0.1%
10 8
 
< 0.1%
9 124
 
0.1%
8 16
 
< 0.1%

time_in_hospital
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4047302
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-01T05:26:15.223768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9888206
Coefficient of variation (CV)0.67854794
Kurtosis0.8358347
Mean4.4047302
Median Absolute Deviation (MAD)2
Skewness1.1293023
Sum445860
Variance8.9330483
MonotonicityNot monotonic
2025-05-01T05:26:15.347954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 17612
17.4%
2 17053
16.8%
1 14110
13.9%
4 13857
13.7%
5 9931
9.8%
6 7523
7.4%
7 5852
 
5.8%
8 4388
 
4.3%
9 3000
 
3.0%
10 2342
 
2.3%
Other values (4) 5555
 
5.5%
ValueCountFrequency (%)
1 14110
13.9%
2 17053
16.8%
3 17612
17.4%
4 13857
13.7%
5 9931
9.8%
6 7523
7.4%
7 5852
 
5.8%
8 4388
 
4.3%
9 3000
 
3.0%
10 2342
 
2.3%
ValueCountFrequency (%)
14 1042
 
1.0%
13 1210
 
1.2%
12 1448
 
1.4%
11 1855
 
1.8%
10 2342
 
2.3%
9 3000
 
3.0%
8 4388
4.3%
7 5852
5.8%
6 7523
7.4%
5 9931
9.8%

num_lab_procedures
Real number (ℝ)

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.103919
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-01T05:26:15.498814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.681372
Coefficient of variation (CV)0.45660285
Kurtosis-0.2477764
Mean43.103919
Median Absolute Deviation (MAD)13
Skewness-0.23447061
Sum4363108
Variance387.35642
MonotonicityNot monotonic
2025-05-01T05:26:15.971381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3166
 
3.1%
43 2781
 
2.7%
44 2472
 
2.4%
45 2353
 
2.3%
38 2205
 
2.2%
40 2189
 
2.2%
46 2175
 
2.1%
41 2105
 
2.1%
42 2104
 
2.1%
47 2097
 
2.1%
Other values (108) 77576
76.6%
ValueCountFrequency (%)
1 3166
3.1%
2 1097
 
1.1%
3 665
 
0.7%
4 377
 
0.4%
5 286
 
0.3%
6 279
 
0.3%
7 323
 
0.3%
8 364
 
0.4%
9 931
 
0.9%
10 837
 
0.8%
ValueCountFrequency (%)
132 1
 
< 0.1%
129 1
 
< 0.1%
126 1
 
< 0.1%
121 1
 
< 0.1%
120 1
 
< 0.1%
118 1
 
< 0.1%
114 2
< 0.1%
113 3
< 0.1%
111 3
< 0.1%
109 4
< 0.1%

num_procedures
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3447141
Minimum0
Maximum6
Zeros46212
Zeros (%)45.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-01T05:26:16.093901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.707102
Coefficient of variation (CV)1.2694907
Kurtosis0.84227892
Mean1.3447141
Median Absolute Deviation (MAD)1
Skewness1.3111571
Sum136116
Variance2.9141974
MonotonicityNot monotonic
2025-05-01T05:26:16.198501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 46212
45.7%
1 20689
20.4%
2 12697
 
12.5%
3 9429
 
9.3%
6 4943
 
4.9%
4 4177
 
4.1%
5 3076
 
3.0%
ValueCountFrequency (%)
0 46212
45.7%
1 20689
20.4%
2 12697
 
12.5%
3 9429
 
9.3%
4 4177
 
4.1%
5 3076
 
3.0%
6 4943
 
4.9%
ValueCountFrequency (%)
6 4943
 
4.9%
5 3076
 
3.0%
4 4177
 
4.1%
3 9429
 
9.3%
2 12697
 
12.5%
1 20689
20.4%
0 46212
45.7%

num_medications
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.036118
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-01T05:26:16.330817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.1401813
Coefficient of variation (CV)0.50761544
Kurtosis3.4507857
Mean16.036118
Median Absolute Deviation (MAD)5
Skewness1.3236994
Sum1623224
Variance66.262551
MonotonicityNot monotonic
2025-05-01T05:26:16.487749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 6032
 
6.0%
12 5939
 
5.9%
15 5754
 
5.7%
11 5753
 
5.7%
14 5662
 
5.6%
16 5391
 
5.3%
10 5298
 
5.2%
17 4888
 
4.8%
9 4879
 
4.8%
18 4503
 
4.4%
Other values (65) 47124
46.6%
ValueCountFrequency (%)
1 262
 
0.3%
2 468
 
0.5%
3 896
 
0.9%
4 1412
 
1.4%
5 2006
 
2.0%
6 2692
2.7%
7 3469
3.4%
8 4337
4.3%
9 4879
4.8%
10 5298
5.2%
ValueCountFrequency (%)
81 1
 
< 0.1%
79 1
 
< 0.1%
75 2
 
< 0.1%
74 1
 
< 0.1%
72 3
< 0.1%
70 2
 
< 0.1%
69 5
< 0.1%
68 7
< 0.1%
67 7
< 0.1%
66 5
< 0.1%

number_diagnoses
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4190648
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-01T05:26:16.632502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9344045
Coefficient of variation (CV)0.26073427
Kurtosis-0.084876655
Mean7.4190648
Median Absolute Deviation (MAD)1
Skewness-0.87317843
Sum750980
Variance3.7419206
MonotonicityNot monotonic
2025-05-01T05:26:16.757596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 49090
48.5%
5 11353
 
11.2%
8 10592
 
10.5%
7 10355
 
10.2%
6 10130
 
10.0%
4 5518
 
5.5%
3 2832
 
2.8%
2 1023
 
1.0%
1 215
 
0.2%
16 45
 
< 0.1%
Other values (6) 70
 
0.1%
ValueCountFrequency (%)
1 215
 
0.2%
2 1023
 
1.0%
3 2832
 
2.8%
4 5518
 
5.5%
5 11353
 
11.2%
6 10130
 
10.0%
7 10355
 
10.2%
8 10592
 
10.5%
9 49090
48.5%
10 17
 
< 0.1%
ValueCountFrequency (%)
16 45
 
< 0.1%
15 10
 
< 0.1%
14 7
 
< 0.1%
13 16
 
< 0.1%
12 9
 
< 0.1%
11 11
 
< 0.1%
10 17
 
< 0.1%
9 49090
48.5%
8 10592
 
10.5%
7 10355
 
10.2%

diabetesMed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
77862 
0
23361 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 77862
76.9%
0 23361
 
23.1%

Length

2025-05-01T05:26:16.883178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-01T05:26:16.984124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 77862
76.9%
0 23361
 
23.1%

Most occurring characters

ValueCountFrequency (%)
1 77862
76.9%
0 23361
 
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 101223
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 77862
76.9%
0 23361
 
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 101223
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 77862
76.9%
0 23361
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 101223
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 77862
76.9%
0 23361
 
23.1%

readmitted
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
NO
54533 
>30
35352 
<30
11338 

Length

Max length3
Median length2
Mean length2.4612588
Min length2

Characters and Unicode

Total characters249136
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd row>30
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO 54533
53.9%
>30 35352
34.9%
<30 11338
 
11.2%

Length

2025-05-01T05:26:17.095587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-01T05:26:17.199489image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
no 54533
53.9%
30 46690
46.1%

Most occurring characters

ValueCountFrequency (%)
N 54533
21.9%
O 54533
21.9%
3 46690
18.7%
0 46690
18.7%
> 35352
14.2%
< 11338
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 249136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 54533
21.9%
O 54533
21.9%
3 46690
18.7%
0 46690
18.7%
> 35352
14.2%
< 11338
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 249136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 54533
21.9%
O 54533
21.9%
3 46690
18.7%
0 46690
18.7%
> 35352
14.2%
< 11338
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 249136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 54533
21.9%
O 54533
21.9%
3 46690
18.7%
0 46690
18.7%
> 35352
14.2%
< 11338
 
4.6%

Interactions

2025-05-01T05:26:12.774085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:04.197149image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:05.259859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:06.332589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:07.655444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:08.653535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:09.713028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:10.724047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:11.735458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:12.878943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:04.349574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:05.363065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:06.428497image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:07.755449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:08.765796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:09.812631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:10.824328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:11.836182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:12.998695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:04.483225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:05.480240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:06.861387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:07.868375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:08.901213image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:09.929942image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:10.938180image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:11.955061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:13.111861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:04.584298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:05.592982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:06.968754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:07.975590image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:09.024096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:10.043150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:11.050861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:12.069301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:13.221115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:04.688279image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:05.703034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:07.078789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:08.080338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:09.139063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:10.153249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:11.155981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:12.181401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:13.336960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:04.797500image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:05.821028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:07.187134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:08.191808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:09.250630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:10.265311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:11.267601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:12.294882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:13.451005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:04.914922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:05.941964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:07.299977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:08.301692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:09.364966image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:10.374959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:11.386325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:12.410061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:13.567476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:05.026887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:06.056783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:07.416093image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:08.413955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:09.475657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:10.486435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:11.502136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:12.525036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:13.685333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:05.150722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:06.194558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:07.538255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:08.530536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:09.596173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:10.606110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:11.619625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-01T05:26:12.649232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-05-01T05:26:17.284817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
admission_source_idadmission_type_idagediabetesMeddischarge_disposition_idnum_lab_proceduresnum_medicationsnum_proceduresnumber_diagnosesreadmittedtime_in_hospital
admission_source_id1.000-0.3810.0490.0180.0430.136-0.063-0.2030.1050.0560.004
admission_type_id-0.3811.000-0.0220.0430.019-0.2240.0860.214-0.1260.045-0.017
age0.049-0.0221.0000.0430.2530.0270.027-0.0580.1960.0380.120
diabetesMed0.0180.0430.0431.0000.0820.0430.1970.0280.0310.0620.071
discharge_disposition_id0.0430.0190.2530.0821.0000.0590.1700.0110.1520.1200.274
num_lab_procedures0.136-0.2240.0270.0430.0591.0000.2510.0230.1700.0310.336
num_medications-0.0630.0860.0270.1970.1700.2511.0000.3520.2940.0630.465
num_procedures-0.2030.214-0.0580.0280.0110.0230.3521.0000.0700.0370.186
number_diagnoses0.105-0.1260.1960.0310.1520.1700.2940.0701.0000.0820.238
readmitted0.0560.0450.0380.0620.1200.0310.0630.0370.0821.0000.047
time_in_hospital0.004-0.0170.1200.0710.2740.3360.4650.1860.2380.0471.000

Missing values

2025-05-01T05:26:13.842957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-01T05:26:14.068169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ageadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalnum_lab_proceduresnum_proceduresnum_medicationsnumber_diagnosesdiabetesMedreadmitted
0562511410110NO
11511735901891>30
22511721151361NO
33511724411671NO
4451171510851NO
55521233161691>30
66531247012171NO
77511757301281>30
885214136822881NO
995334123331881NO
ageadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalnum_lab_proceduresnum_proceduresnum_medicationsnumber_diagnosesdiabetesMedreadmitted
1017566511724661791>30
1017577511752111691NO
1017588511757612291NO
101759851171101571NO
1017606511764512591>30
1017617513735101691>30
1017628514553331891NO
1017637511715309131NO
10176485237104522191NO
101765751176133390NO